Comparison of Malaria Simulations Driven by Meteorological Observations and Reanalysis Products in Senegal

Ibrahima Diouf, Belen Rodriguez-Fonseca, Abdoulaye Deme, Cyril Caminade, Andrew P. Morse, Moustapha Cisse, Ibrahima Sy, Ibrahima Dia, Volker Ermert, Jacques-André Ndione and Amadou Thierno Gaye
Additional contact information
Ibrahima Diouf: Laboratoire de Physique de l’Atmosphère et de l’Océan-Siméon Fongang, Ecole Supérieure Polytechnique de l’Université Cheikh Anta Diop (UCAD), BP 5085, Dakar-Fann, Dakar 10700, Senegal
Belen Rodriguez-Fonseca: Department of Geophysics and Meteorology, Universidad Complutense de, Plaza de las Ciencias s/n, Madrid 28040, Spain
Abdoulaye Deme: Unité de Formation et de Recherche de Sciences Appliquées et de Technologie, Université Gaston Berger de Saint-Louis, BP 234, Saint-Louis 32000, Senegal
Cyril Caminade: Department of Epidemiology and Population Health, Institute of Infection and Global Health, University of Liverpool, Water House Building, Liverpool L693GL, UK
Andrew P. Morse: National Institute for Health Research [M1] (NIHR), Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool L69 3GL, UK
Moustapha Cisse: Programme National de Lutte contre le Paludisme (PNLP), BP 25 270 Dakar-Fann, Dakar 10700, Senegal
Ibrahima Sy: Centre de Suivi Ecologique, BP 15532, Fann Résidense, Dakar 10700, Senegal
Ibrahima Dia: Institut Pasteur de Dakar (IPD), Unité d’Entomologie Médicale, 36 Av. Pasteur, BP 220 Dakar, Dakar 12900, Senegal
Volker Ermert: Institute of Geophysics and Meteorology, University of Cologne, Kerpenerstr. 13, D-50923 Cologne, Germany
Jacques-André Ndione: Centre de Suivi Ecologique, BP 15532, Fann Résidense, Dakar 10700, Senegal
Amadou Thierno Gaye: Laboratoire de Physique de l’Atmosphère et de l’Océan-Siméon Fongang, Ecole Supérieure Polytechnique de l’Université Cheikh Anta Diop (UCAD), BP 5085, Dakar-Fann, Dakar 10700, Senegal

IJERPH, 2017, vol. 14, issue 10, 1-20

Abstract: The analysis of the spatial and temporal variability of climate parameters is crucial to study the impact of climate-sensitive vector-borne diseases such as malaria. The use of malaria models is an alternative way of producing potential malaria historical data for Senegal due to the lack of reliable observations for malaria outbreaks over a long time period. Consequently, here we use the Liverpool Malaria Model (LMM), driven by different climatic datasets, in order to study and validate simulated malaria parameters over Senegal. The findings confirm that the risk of malaria transmission is mainly linked to climate variables such as rainfall and temperature as well as specific landscape characteristics. For the whole of Senegal, a lag of two months is generally observed between the peak of rainfall in August and the maximum number of reported malaria cases in October. The malaria transmission season usually takes place from September to November, corresponding to the second peak of temperature occurring in October. Observed malaria data from the Programme National de Lutte contre le Paludisme (PNLP, National Malaria control Programme in Senegal) and outputs from the meteorological data used in this study were compared. The malaria model outputs present some consistencies with observed malaria dynamics over Senegal, and further allow the exploration of simulations performed with reanalysis data sets over a longer time period. The simulated malaria risk significantly decreased during the 1970s and 1980s over Senegal. This result is consistent with the observed decrease of malaria vectors and malaria cases reported by field entomologists and clinicians in the literature. The main differences between model outputs and observations regard amplitude, but can be related not only to reanalysis deficiencies but also to other environmental and socio-economic factors that are not included in this mechanistic malaria model framework. The present study can be considered as a validation of the reliability of reanalysis to be used as inputs for the calculation of malaria parameters in the Sahel using dynamical malaria models.

Keywords: climate; malaria; observations; simulations; stations; Senegal; model (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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